{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.uniform(-10, 10, size = 100)\n",
    "y = 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3w+kVaDg9yVEKd5E+Vm3fT0dPRGfJSE5TuIv0UVXdyIiSMBfP0nB6krsU7iIJeiNRfrm5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\nIYW7iEgeUriLiOSh/w9XxkSwW9RZKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11603a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sort(x), y[np.argsort(x)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
